Data Quality Remediation
In the previous chapter, we described how to set up data quality reporting, which allows you to easily identify bad data. This chapter moves on to correcting the data. As explained back in Chapter 1, this does not mean that the organization should aim for perfect data. The aim should be to get the data to the level where it no longer causes significant impediments to the organization achieving its goals.
This is often seen as the most challenging part of the data quality initiative. There is typically a major resource investment and a long lead time to make progress.
In spite of these challenges, this phase is also an exciting one. This is where the organization starts to see the tangible benefits that we attempted to estimate back in Chapter 3. As the bad data is replaced with correct data, the issues experienced prior to the initiative finally start to reduce in severity and impact.
Processes become more efficient, resource challenges driven by poor...
